Extracting and Visualizing Structural Features in Environmental Point Cloud LiDaR Data Sets

We present a user-assisted approach to extracting and visualizing structural features from point clouds obtained by terrestrial and airborne laser scanning devices. We apply a multi-scale approach to express the membership of local point environments to corresponding geometric shape classes in terms of probability. The structural classes we are dealing with are surfaces, curves, and critical points. Based on user-defined parameters we determine individual feature values for each of these classes. This information is filtered and combined to establish feature graphs which can be visualized in combination with the color-encoded feature and structural probability estimates of the measured raw point data. Our method can be used, for example, for exploring geological point data scanned from multiple viewpoints. The combined visualization of the raw data together with curvature, structural probability and extracted features provides a scientifically valid and useful alternative to surface reconstruction since topological ambiguities may be less significant.